Assessing production fulfillment time risk: application to pandemic-related health equipment

Abstract

Manufacturing companies strive to identify and manage the effects of unexpected disruptions (risks) on their production processes, which affect their performance and resilience. In this study, we propose a decision framework to capture the impact of interconnected risk sources, on the efficiency of manufacturing companies. The proposed framework utilises a novel mixed-integer linear programming (MILP) model to minimize the time of satisfying the orders while it considers the risk associated with suppliers and manufacturers. The MILP model considers the relationships among (i) material and suppliers and (ii) work centers to measure the propagation of risks throughout the production system. The proposed framework also utilises the Monte Carlo simulation to calculate the associated likelihood of delay and the distribution of the delivery time of orders. To show the complication of the propagation of risk, two distinct scenarios are compared. The first scenario considers zero risks, while the second one assigns probabilistic risk to the suppliers and work centers. The results highlight the magnitude and the complexity of the risk propagation from various interconnected sources through the production system. It also identifies the most vulnerable components of the production system affected more by various types of risk.

KEYWORDS:

• Risk assessment
• resilient supply chain
• production planning
• mixed-integer linear programming
• Monte Carlo simulation

Data availability

The data that support the findings of this study are available from the corresponding author, KB, upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Leili Soltanisehat

Leili Soltanisehat is a visiting assistant professor of data analytics in the Finance and Operations Management Department at the University of Tulsa, OK. She is a Ph.D. candidate in Industrial and Systems Engineering at the University of Oklahoma. Leili’s research interests include data analytics, operation research, and modeling and simulation applicable to healthcare, supply chain, critical infrastructure, blockchain, and energy. Her work has appeared in IISE Transactions on Healthcare Systems Engineering, Renewable and Sustainable Energy Reviews, IEEE Transactions on Engineering Management, Energy Economics, and the Journal of Energy Policy, among others.

Nafiseh Ghorbani-Renani

Nafiseh Ghorbani-Renani received the B.S. and M.S. degrees in industrial engineering, respectively from Yazd University in Iran and UTM University in Malaysia. She completed the Ph.D. in industrial and systems engineering at the University of Oklahoma, Norman, OK, USA. Her work, which has appeared in Reliability Engineering and System Safety and Computers and Industrial Engineering, among others, focuses on the control of risk and resilience in interconnected networks (e.g. interdependent infrastructure networks, production networks, supply chain and transshipment networks) against failures or the risk of delays. She is currently a postdoctoral scholar at Stevens Institute of Technology, School of Systems and Enterprises, where her work deals with the optimization of peer-to-peer energy trading in microgrids.

Andrés D. González

Andréás D. González received the Ph.D. degree in civil engineering from Rice University, Houston, TX, USA, and in engineering from Universidad de los Andes, Bogotá, Colombia. He also holds a Six-Sigma Black Belt from Arizona State University, Tempe, AZ, USA, the M.Eng. degree in industrial engineering and the B.Sc. degree in physics from Universidad de los Andes. He is currently an Assistant Professor with the School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK, USA. His research interests include developing and applying analytical tools from systems dynamics, statistical physics, operations research, and civil engineering to study the dynamics associated with social and physical systems. He has worked on modeling the behavior of financial markets, designing routes and frequencies of massive transportation systems, and more recently on optimising the resilience of critical interdependent infrastructure networks.

Kash Barker

Kash Barker received the B.S. and M.S. degrees in industrial engineering from the University of Oklahoma, Norman, OK, USA, and the Ph.D. degree in systems engineering from the University of Virginia, Charlottesville, VA, USA. He is currently a David L. Boren Professor and an Anadarko Petroleum Corporation Presidential Professor with the School of Industrial and Systems Engineering, University of Oklahoma. His work dealing with the reliability, resilience, and economic impacts of cyber-physical-social systems has been funded by the National Science Foundation, Department of Transportation, and Army Research Office, among others, and has resulted in over 80 refereed journal publications. He is an Associate Editor for the IISE Transactions and Naval Research Logistics, and he is on the editorial board of Risk Analysis.